The present invention relates in general to computing systems, and more specifically to resistive processing unit (RPU) devices with hysteretic updates for neural network training.
“Machine learning” is used to broadly describe a primary function of electronic systems that learn from data. In machine learning and cognitive science, artificial neural networks (ANNs) or deep neural networks (DNNs) are a family of statistical learning models inspired by the biological neural networks of animals, and in particular the brain. ANNs can be used to estimate or approximate systems and functions that depend on a large number of inputs and are generally unknown. ANNs are formed from crossbar arrays of two-terminal RPUs. Crossbar arrays are high density, low cost circuit architectures used to form a variety of electronic circuits and devices, including ANN architectures, neuromorphic microchips and ultra-high density nonvolatile memory. A basic crossbar array configuration includes a set of conductive row wires and a set of conductive column wires formed to intersect the set of conductive row wires. The intersections between the two sets of wires are separated by so-called cross-point devices, which can be formed from thin film material.